Various obstacles exist in order to analyze large volumes of multi-dimensional data. Queries on large multi-dimensional datasets typically are a relatively slow operation due to processes required on setup time, search time, and connection time. Further, most query specifications are limited to attributes and categories. As such, complex and multiple queries are required to query on a specific value in a transaction record. Further, traditional query results are written in pages or listings which can be difficult to interpret.
Prior data comparison techniques include simple graphical techniques, such as bar charts, pie charts, and x-y charts. These simple graphical techniques are easy to use but offer limited information when analyzing large amounts of business data. For example, simple bar charts or pie charts only show highly aggregated data. Drilldown techniques can be employed. Such techniques, however, merely allow users to drill down by attributes or categories.
In order to view specific content of a large multi-dimensional dataset, numerous and time-consuming queries are often necessary since query specifications are limited to attributes or categories of the dataset. Complex and multiple queries are required to query a specific value in a transaction record. Multiple queries often require manual evaluation of vast amounts of data. In some instances, the query results are written in multiple pages of search results or listings. Users are required to manually review the listings to find information on specific content or values in the data.